


import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_SCREAMING_SNAKE_CASE     :					List[str]      =       logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     :					Any      =       {"vocab_file": "sentencepiece.model"}

_SCREAMING_SNAKE_CASE     :					Any      =       {
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
}

_SCREAMING_SNAKE_CASE     :					Optional[int]      =       {
    "google/rembert": 2_56,
}

class     A__   (       snake_case__      ):







    """simple docstring"""

    __magic_name__         =						VOCAB_FILES_NAMES
    __magic_name__         =						PRETRAINED_VOCAB_FILES_MAP
    __magic_name__         =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    def __init__(			self     ,   __snake_case     ,   __snake_case=False     ,   __snake_case=True     ,   __snake_case=True     ,   __snake_case="[CLS]"     ,   __snake_case="[SEP]"     ,   __snake_case="[UNK]"     ,   __snake_case="[SEP]"     ,   __snake_case="[PAD]"     ,   __snake_case="[CLS]"     ,   __snake_case="[MASK]"     ,   **__snake_case     ,   ):
         super().__init__(
             do_lower_case=__snake_case     ,   remove_space=__snake_case     ,   keep_accents=__snake_case     ,   bos_token=__snake_case     ,   eos_token=__snake_case     ,   unk_token=__snake_case     ,   sep_token=__snake_case     ,   pad_token=__snake_case     ,   cls_token=__snake_case     ,   mask_token=__snake_case     ,   **__snake_case     ,   )

         snake_case											=							do_lower_case
         snake_case											=							remove_space
         snake_case											=							keep_accents
         snake_case											=							vocab_file

         snake_case											=							spm.SentencePieceProcessor()
         self.sp_model.Load(__snake_case       )
    @property
    def 						a_     (			self       ):
         return len(self.sp_model       )
    def 						a_     (			self       ):
         snake_case											=							{self.convert_ids_to_tokens(__snake_case       ): i for i in range(self.vocab_size       )}
         vocab.update(self.added_tokens_encoder       )
         return vocab
    def __getstate__(			self       ):
         snake_case											=							self.__dict__.copy()
         snake_case											=							None
         return state
    def __setstate__(			self     ,   __snake_case       ):
         snake_case											=							d
         snake_case											=							spm.SentencePieceProcessor()
         self.sp_model.Load(self.vocab_file       )
    def 						a_     (			self     ,   __snake_case     ,   __snake_case=False       ):
         snake_case											=							self.sp_model.EncodeAsPieces(__snake_case       )
         return pieces
    def 						a_     (			self     ,   __snake_case       ):
         return self.sp_model.PieceToId(__snake_case       )
    def 						a_     (			self     ,   __snake_case       ):
         return self.sp_model.IdToPiece(__snake_case       )
    def 						a_     (			self     ,   __snake_case       ):
         snake_case											=							self.sp_model.decode_pieces(__snake_case       )
         return out_string
    def 						a_     (			self     ,   __snake_case     ,   __snake_case = None       ):
         snake_case											=							[self.sep_token_id]
         snake_case											=							[self.cls_token_id]
         if token_ids_a is None:
              return cls + token_ids_a + sep
         return cls + token_ids_a + sep + token_ids_a + sep
    def 						a_     (			self     ,   __snake_case     ,   __snake_case = None     ,   __snake_case = False       ):

         if already_has_special_tokens:
              if token_ids_a is not None:
                   raise ValueError(
                       '''You should not supply a second sequence if the provided sequence of '''
                       '''ids is already formatted with special tokens for the model.'''       )
              return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

         if token_ids_a is not None:
              return [1] + ([0] * len(__snake_case       )) + [1] + ([0] * len(__snake_case       )) + [1]
         return [1] + ([0] * len(__snake_case       )) + [1]
    def 						a_     (			self     ,   __snake_case     ,   __snake_case = None       ):
         snake_case											=							[self.sep_token_id]
         snake_case											=							[self.cls_token_id]

         if token_ids_a is None:
              return len(cls + token_ids_a + sep       ) * [0]
         return len(cls + token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1]




    def 						a_     (			self     ,   __snake_case     ,   __snake_case = None       ):
         if not os.path.isdir(__snake_case       ):
              logger.error('''Vocabulary path ({}) should be a directory'''.format(__snake_case       )       )
              return
         snake_case											=							os.path.join(
             __snake_case     ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )

         if os.path.abspath(self.vocab_file       ) != os.path.abspath(__snake_case       ):
              copyfile(self.vocab_file     ,   __snake_case       )

         return (out_vocab_file,)








from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
 from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline




